In most cases, evolutionary models ask neither whether humans actually learn based on strict payoff maximization nor whether subjects have an incentive to do so. Instead, it is taken as a tacit assumption that immediate payoff maximization serves an individual's best interest [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. This assumption might be justified when the players' interactions lack any strategic component (as in single-player optimization), or when the players' goals are fully aligned or misaligned (as in partnership and zero-sum games, respectively). However, the rationale for selfish learning is less clear in social dilemmas that entail a mixture of cooperative and competitive motives [8]}, [9]}, [10]}, [11]}. When social dilemmas give rise to multiple equilibria, selfish optimization may easily result in detrimental outcomes wherein everyone is worse off. If evolution acts not only upon the players' strategies but also upon the learning rules that determine how players choose their strategies in the first place, then it may select for different learning rules altogether.
